\section*{Naïve Bayes Classifier}
The Naïve Bayes Classifier is used to classify if a review is either positive or negative. The classifier first requires some training data, where each review is marked with a score. The classifier then learns how negative/positive each word is, and saves this dictionary for classification of future reviews. 

When the classifier has learned the training data, it can take a review, and based on the tokens used in the review, it will calculate a score. To avoid ending up with zero probabilities, laplace smoothening is used. Thereby the minimum probability of a given word will always be the $1/|C|$ where $|C|$ is the number of possible sentiments. 